grip task 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0eee32c7",
   "metadata": {},
   "source": [
    "# Task 1 -> Prediction using Supervised ML\n",
    "\n",
    "# Author: Syed Faisal Ali\n",
    "\n",
    "# DataSet link: http://bit.ly/w-data\n",
    "\n",
    "# Problem: predict percentage of students based on number of study hours."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94590d1a",
   "metadata": {},
   "source": [
    "## **Linear Regression with Python Scikit Learn**\n",
    "In this section we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables.\n",
    "\n",
    "### **Simple Linear Regression**\n",
    "In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af7789da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%` not found.\n"
     ]
    }
   ],
   "source": [
    "# importing libraries to be used\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score\n",
    "% matplotlib.inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "587f5ca5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "url = \"http://bit.ly/w-data\"\n",
    "df = pd.read_csv(url)\n",
    "print(\"Data imported successfully\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f8eede",
   "metadata": {},
   "source": [
    "## Summary of  data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b773e469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34a42b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbbd8f7",
   "metadata": {},
   "source": [
    "## Plotting 'No: of Hourse' vs 'Score percentage' to check the linear Relationship in data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dba55066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.Hours,df.Scores,color='blue')\n",
    "plt.title('No: of Hourse vs Score percentage')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Score percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc312edd",
   "metadata": {},
   "source": [
    "## Splitting training and testing data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd41dfd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "msk=np.random.rand(len(df))< 0.75\n",
    "train=df[msk]\n",
    "test=df[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce23c41",
   "metadata": {},
   "source": [
    "## Training model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c621a27c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients: 9.78\n",
      "intercept: 2.83\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "regr=linear_model.LinearRegression()\n",
    "\n",
    "x_train=train[['Hours']]\n",
    "y_train=train[['Scores']]\n",
    "\n",
    "x_test=test[['Hours']]\n",
    "y_test=test[['Scores']]\n",
    "\n",
    "regr.fit(x_train,y_train)\n",
    "print('coefficients: %.2f'% regr.coef_)\n",
    "print('intercept: %.2f' % regr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c835f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat=regr.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b47fe2d",
   "metadata": {},
   "source": [
    "## Checking Accuracy of the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a6942143",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual Square Error: 30.52\n",
      "r2_score: 0.94\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IceCandyman\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3430: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
      "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print('Residual Square Error: %.2f'% np.mean((y_test-y_hat)**2))\n",
    "print('r2_score: %.2f' % r2_score(y_test,y_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a64ecb",
   "metadata": {},
   "source": [
    "## Line of Best Fit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ab142fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.Hours,df.Scores,color='blue')\n",
    "plt.plot(x_train,regr.intercept_ + regr.coef_*x_train,'-r')\n",
    "plt.title('No: of Hourse vs Scores')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}